Discriminative structural approaches for enzyme active-site prediction

<p>Abstract</p> <p>Background</p> <p>Predicting enzyme active-sites in proteins is an important issue not only for protein sciences but also for a variety of practical applications such as drug design. Because enzyme reaction mechanisms are based on the local structures of enzyme active-sites, various template-based methods that compare local structures in proteins have been developed to date. In comparing such local sites, a simple measurement, RMSD, has been used so far.</p> <p>Results</p> <p>This paper introduces new machine learning algorithms that refine the similarity/deviation for comparison of local structures. The similarity/deviation is applied to two types of applications, single template analysis and multiple template analysis. In the single template analysis, a single template is used as a query to search proteins for active sites, whereas a protein structure is examined as a query to discover the possible active-sites using a set of templates in the multiple template analysis.</p> <p>Conclusions</p> <p>This paper experimentally illustrates that the machine learning algorithms effectively improve the similarity/deviation measurements for both the analyses.</p

SAHG, a comprehensive database of predicted structures of all human proteins

Most proteins from higher organisms are known to be multi-domain proteins and contain substantial numbers of intrinsically disordered (ID) regions. To analyse such protein sequences, those from human for instance, we developed a special protein-structure-prediction pipeline and accumulated the products in the Structure Atlas of Human Genome (SAHG) database at http://bird.cbrc.jp/sahg. With the pipeline, human proteins were examined by local alignment methods (BLAST, PSI-BLAST and Smith–Waterman profile–profile alignment), global–local alignment methods (FORTE) and prediction tools for ID regions (POODLE-S) and homology modeling (MODELLER). Conformational changes of protein models upon ligand-binding were predicted by simultaneous modeling using templates of apo and holo forms. When there were no suitable templates for holo forms and the apo models were accurate, we prepared holo models using prediction methods for ligand-binding (eF-seek) and conformational change (the elastic network model and the linear response theory). Models are displayed as animated images. As of July 2010, SAHG contains 42 581 protein-domain models in approximately 24 900 unique human protein sequences from the RefSeq database. Annotation of models with functional information and links to other databases such as EzCatDB, InterPro or HPRD are also provided to facilitate understanding the protein structure-function relationships

Creating a specialist protein resource network:a meeting report for the protein bioinformatics and community resources retreat

During 11–12 August 2014, a Protein Bioinformatics and Community Resources Retreat was held at the Wellcome Trust Genome Campus in Hinxton, UK. This meeting brought together the principal investigators of several specialized protein resources (such as CAZy, TCDB and MEROPS) as well as those from protein databases from the large Bioinformatics centres (including UniProt and RefSeq). The retreat was divided into five sessions: (1) key challenges, (2) the databases represented, (3) best practices for maintenance and curation, (4) information flow to and from large data centers and (5) communication and funding. An important outcome of this meeting was the creation of a Specialist Protein Resource Network that we believe will improve coordination of the activities of its member resources. We invite further protein database resources to join the network and continue the dialogue

A snapshot of 3D-view of catalytic mechanism (3D-EzCat applet): in this snapshot, the yellow arc arrow indicates that the oxygen atom of ‘CAQ’ molecule makes a nucleophilic attack on the methyl group of ‘SAM’ molecule

<p><b>Copyright information:</b></p><p>Taken from "EzCatDB: the Enzyme Catalytic-mechanism Database"</p><p>Nucleic Acids Research 2004 ;33(Database Issue):D407-D412.</p><p>Published online 17 Dec 2004 </p><p>PMCID:PMC540034.</p><p>Copyright © 2005 Oxford University Press</p

Strong hydrophobic nature of cysteine residues in proteins

AbstractThe differences between disulfide-bonding cystine (Cys_SS) and free cysteine (Cys_SH) residues were examined by analyzing the statistical distribution of both types of residue in proteins of known structure. Surprisingly, Cys_SH residues display stronger hydrophobicity than Cys_SS residues. A detailed survey of atoms which come into contact with the sulfhydryl group (sulfur atom) of Cys_SH revealed those atoms are essentially the same in number and variety as those of the methyl group of isoleucine, but are quite different to those of the hydroxyl group of serine. Moreover, the relationships among amino acids were also determined using the 3D-profile table of known protein structures. Cys_SH was located in the hydrophobic cluster, along with residues such as Met, Trp and Tyr, and was clearly separated from Ser and Thr in the polar cluster. These results imply that free cysteines behave as strongly hydrophobic, and not hydrophilic, residues in proteins

Motif-based searching in TOPS protein topology databases

Motivation: TOPS cartoons are a schematic abstraction of protein 3-dimensional structures in two dimensions, and are used for understanding and manual comparison of protein folds. Recently an algorithm that produces the cartoons automatically from protein structures has been devised and cartoons have been generated to represent all the structures in the structural databank. There is now a need to be able to define target topological patterns and to search the database for matching domains. Results: We have devised a formal language for describing TOPS diagrams and patterns, and have designed an efficient algorithm to match a pattern to a set of diagrams. A pattern-matching system has been implemented, and tested on a database derived from all the current entries in the Protein Data Bank (15,000 domains). Users can search on patterns selected from a library of motifs, or alternatively they can define their own search patterns. Availability: The system is accessible over the Web at htt..